IDENTIFICATION AND MEASUREMENT OF THE CRITICAL SUCCESS FACTORS IN COMPANIES THAT EMPLOY ARTIFICIAL NEURAL NETWORKS

• DOI: 10.22533/at.ed.3172282202129

• Palavras-chave: Artificial Neural Networks, Managerial Indicators, Critical Success Factors

• Keywords: Artificial Neural Networks, Managerial Indicators, Critical Success Factors

• Abstract:

  The way in which processes are developed in the industry are fundamental to the success of organizations and it is imperative that strategies to reduce costs and increase the efficiency of operations are adopted. The so-called Industry 4.0 is characterized by being the fourth industrial generation and is expected to offer improvements in industrial processes involving: operation, engineering, production planning and control, logistics, and continuous analysis during the life cycle of products and services . In this context of Industry 4.0, important issues, inquiries and problems arise, such as assessing whether the use of Industry 4.0 technologies can help in identifying and measuring indicators and consequently increase managerial effectiveness in the decision-making process of organizations? To investigate and try to answer the question presented, Artificial Neural Networks - ANNs are being implemented. ANNs are computational models that seek to simulate the behavior of biological neural networks and are used in several computational and organizational problems. In this work, the research was conducted using the Design Science Research (DSR) method. It should be noted that the DSR seeks to guide research whose objective is to prescribe solutions, considering the design and evaluation of artifacts aimed at solving practical problems in different contexts. Among the contexts in which the DSR fits as an adequate methodological approach, research carried out in the scope of operations management, information technology, engineering, among others, stand out, which is in line with this work proposal. The work was developed, initially obtaining and identifying managerial indicators in organizations for the decision-making process. These indicators are the input data for the ANNs. Through the ANNs, it is intended to be able to observe and identify the relevance of each managerial indicator in organizational success. In this way, management will be able to focus attention on the most relevant indicators for organizational success. The work is under development and the first results obtained demonstrate the ability and convenience of using ANNs in the approach.